qamsysterm

精通Matlab综合辅导与指南-源程序，跟书籍配套的(Proficient in Matlab comprehensive counseling and guidance- source)

redresor mono, pictures, matlab simulation

compressive sensing ALGORITHIM CODE FOR MATLAB

codes.rar consists some file for aco optimumization

本程序采用自相关法求混沌时间序列重构的时间延迟。(This procedure using the autocorrelation method for chaotic time series reconstruction time delay.)

窗函数法设计数字滤波器 矩形窗、海宁窗、汉明窗和布莱克曼窗 滤波特性(Window function method to design digital filters rectangular windows, Haining window, Hamming window and Blackman window filtering properties)

动态规划算法的设计与实现本科毕业论文，已经通过的(Dynamic programming algorithm design and implementation of undergraduate thesis, already adopted by the)

LDPC的解码程序，可以更理解LDBC的解码过程(LDPC decoding process can be more to understand LDBC decoding process)

seperate linear seperable and non-linearly patters

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.